Addicted to email? Email overload complaints? Here’s a suggested practical solution…

This could be a new way to manage your email, set realistic expectations for others and …. most importantly, help to be present in the present.

Coping with Email Overload – Peter Bregman – Harvard Business Review.

Patients choose hospitals based on social media

The latest report from Pricewaterhouse Coopers – as reported in Fiercehealthcare.

In a survey of more than a thousand consumers, more than two-fifths of individuals said social media did affect their choice of a provider or organization. Forty-five percent said it would affect their decision to get a second opinion; 34 percent said it would influence their decision about taking a certain medication and 32 percent said it would affect their choice of a health insurance plan.

The PwC report follows a study last summer by hospital market research firm YouGov Healthcare, which found that 57 percent of consumers said a hospital’s social media connections would strongly affect their decision to receive treatment at that facility.

Read more: Patients choose hospitals based on social media – FierceHealthcare http://www.fiercehealthcare.com/story/patients-choose-hospitals-based-social-media/2012-04-19?goback=.gmp_3711160.gde_3711160_member_109141266#ixzz1tG6KnHSB
Subscribe: http://www.fiercehealthcare.com/signup?sourceform=Viral-Tynt-FierceHealthcare-FierceHealthcare

Physicians and online training – a promising match!

A press release jointly issued today by ON24, Inc. and MedData Group revealed the results of a 971-physician survey about digital behavior.  A whopping 84.1% of the surveyed doctors said they would prefer to attend CME training online, albeit in stark contrast to the 6.4% who reported actual participation in virtual events.  Nonetheless that’s pretty solid validation that efforts at UCSF to offer online classes are going to be a welcomed option for our busy trainees! 

To read more about the interesting survey findings, click here.

Measuring federal social media interaction rates—and how UCSF fares

I love Expert Labs‘ new Federal Social Media Index, a unified dashboard of Twitter interaction stats for 125 different federal agencies. The effort itself is quite impressive, but the stats are even better.

Most agencies have a large number of followers, but a minuscule number of people actually responding to queries. If the point of social media is to be social, agencies are doing a fairly poor job.

How are UCSF Twitter accounts faring? I tried searching Twitter for replies to queries from several UCSF accounts from the morning of April 10 to the morning of April 14 (this excludes retweets and mentions).

The results?

  • @ucsf: 0 replies
  • @ctsiatucsf: 1 reply (a thank you from the UCSF library)
  • @gladstonelabs: 1 reply (a thank you from Bay Area Malaria)
  • @ucsf_library: 0 replies
  • @ucsfdentistry: 0 replies
  • @ucsfmedicine: 0 replies

For better or for worse, we’re doing about as well as the federal government.

Read more:

Describing the Difference Research Has Made to the World

Here is an interesting new blog post by Heather Piwowar about the different ways research can impact the world and the importance of telling them apart. Good food for thought as we think about ways to help researchers analyze how people are reading, bookmarking, sharing, discussing, and citing research online.

I think Anirvan made a great point to think about ways how we can integrate “altmetrics” data with UCSF Profiles. Some of the metrics mentioned below may be a great starting point.

Here’s what Heather writes:

Figuring out the flavors of science impact. CC-BY-NC by maniacyak on flickr

We have clustered all PLoS ONE papers published before 2010 using five metrics that are fairly distinct from one another: HTML article page views, number of Mendeley reader bookmarks, Faculty of 1000 score, Web of Science citation counts as of 2011, and a combo count of twitter, Facebook, delicious, and blog discussion.

We normalized the metrics to account for differences due to publication date and service popularity, transformed them, and standardized to a common scale. We tried lots of cluster possibilities; it seems that five clusters fit this particular sample the best.

Here is a taste of the clusters we found.  Bright blue in the figure below means that the metric has high values in that cluster, dark grey means the metric doesn’t have much activity.  For example, papers in “flavour E” in the first column have fairly low scores on all five metrics, whereas papers in “flavour C” on the far right have a lot of HTML page views and Sharing (blog posts, tweeting, facebook clicking, etc) activity. View image and read on

Further reading:

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